Time-varying modelling of arbitrary non-stationary signals

A novel approach to time-varying signal modeling is presented to reliably identify rational models in arbitrary nonstationary environments. It is motivated by the shortcomings of both adaptation methods, which cannot handle arbitrary nonstationarities, and description techniques, which tend to use unjustifiable assumptions on the observed data. Only limited a priori knowledge about the nonstationarity, namely, the expected maximum rate of change of the model parameters, is necessary to estimate these parameters online. The criterion considered is a constrained-least-squares cost functional which incorporates with equal weight all instantaneous errors up to the time of observation. An appropriate algorithm is developed and its performance is discussed to illustrate the increased confidence in time-varying model estimation which results from the approach